Model Predictive Control (MPC) is the most widely used advanced process control technology applied in process industries. There are more than 10,000 worldwide applications currently in service. A MPC controller relies on a model to predict the behavior of a process (the controlled variables, CV) and makes changes to the manipulated variables (MV) so that it can keep the process running inside a prescribed constraint set. An excellent model is a prerequisite to a successful implementation of MPC. Successful models generally have two common characteristics.
First, the predictions from the model must closely match the observed real world behavior of the process. This places a minimum accuracy requirement on the so-called “forward model.” The model dynamics must be sufficiently accurate to predict towards a steady state even during a large dynamic transient, and the steady state gains needs to be sufficiently accurate to predict where the process will line out, since the control target optimization (e.g. Linear Program, or LP) calculation will be initialized at the predicted steady state value expected to be achieved several hours into the future.
Second, the behavior of the Linear Program is dependent on the accuracy of the inverse gain matrix, the so-called “reverse model.” Small errors in the gain matrix can lead to large errors in the inverse gain matrix under adverse worst case conditions, and the MVs can easily move in the wrong direction and cause poor control performance as well as system instability under these conditions. Ideally, the control actions based on the model must also be understood by the users of the MPC and must match their engineering judgment. This means that the predictions from the model inverse must closely match the observed real world behavior.
The importance of the first point is well-known to users of MPC controllers. Long periods of step-testing the process by manipulating process inputs is often required. It is necessary to sift through historical data and carefully discard data contaminated by unmeasured disturbances. The importance of the second point has also recently been viewed by many users as vital to the success of MPC applications and MPC controllers. When the initial model is created and when model updates are performed during subsequent maintenance work, it is vital that the engineering judgments about proper control action be imposed on the model, i.e. MVs need to move in the correct direction and by approximately the correct amount.